Did Historical Storms Used in Probable Maximum Precipitation (PMP) Estimation Reach Maximum Efficiency? A Large Model Ensemble Approach

Abstract

The flood that would result from the greatest depth of precipitation “meteorologically possible”, or Probable Maximum Precipitation (PMP) is used in the design of dam spillways and other high-risk structures. Historically, PMP has been estimated by scaling depth-area-duration relationships obtained from severe historical storms. Over the last decade, numerical weather prediction models have been used to instead simulate precipitation resulting from the addition of atmospheric moisture (called relative humidity maximization, or RHM). Despite the major improvement this represents, model-based PMP relies on a key assumption, which this paper re-evaluates in Oroville dam's Feather River watershed (California). Model-based as well as earlier procedures assume that severe historical storms achieved maximum efficiency (moisture conversion to precipitation) and only maximize moisture. We examine the most severe storms found in the CESM2-LE global climate model ensemble, which constitutes a very large artificial record (∼1,150 years) in comparison with the historical record, to understand the upper bounds of storm efficiency and precipitation. We downscale the 10 most severe CESM2-LE storms (by precipitation totals), and identify key storm attributes (vertical motion, convection and convergence) that control precipitation efficiency. In comparison with historical storms, we find that CESM-LE storms can have 30% higher efficiency and 32% higher precipitation, but produce only 8% higher PMP estimates, suggesting some convergence of model ensemble and historical storms in terms of PMP. The understanding of the controls on storm efficiency that our work provides leverages past work focused on moisture and supports the development of more reliable PMP storm amplification guidance.